An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)

Abstract. Uncertain parameters in physical parameterizations of General Circulation Models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs but most of the optimization methods are unconstrained optimization methods under a given performance indicator, so that the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at top of model, which is known for its importance to the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using global means of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. And the radiation constraint is defined as the deviation of the net longwave flux at top of model (FLNT) and net solar flux at top of model (FSNT) less than 1 W m −2 . Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) as well as the radiation imbalance is as low as 0.1 W m −2 . The proposed method provides the insight for physics-guided optimization under the premise of a profound understanding of models and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.